/*
 * Copyright (c) 2017 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp"

namespace winograd
{
  template <>
  template <>
  void WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::execute(
    const int n_output_channels,
    const int n_input_channels,
    const float* const input,
    float* const output,
    const int matrix_stride,
    const int matrix_row_stride
  )
  {
    // Get pointers to each cell of the weight tensor
    const auto weight_col_stride = n_input_channels * n_output_channels;
    const auto weight_row_stride = 5 * weight_col_stride;
    const float *inptrs[5][5];
    for (int i = 0; i < 5; i++)
    {
      for (int j = 0; j < 5; j++)
      {
        inptrs[i][j] = input + i*weight_row_stride + j*weight_col_stride;
      }
    }

    // For each input channel
    for (int ic = 0; ic < n_input_channels; ic++)
    {
      float *outptr = output + ic * matrix_row_stride;

      // For each output channel
      int channels_remaining = n_output_channels;
#ifdef __aarch64__
      for (; channels_remaining >= 4; channels_remaining -= 4)
      {
        // Matrices used and computed in this kernel
        float32x4_t w[5][5], Ww[6][5], V[6][6];

        // Read weights
        for (int i = 0; i < 5; i++)
        {
          for (int j = 0; j < 5; j++)
          {
            w[i][j] = vld1q_f32(inptrs[i][j]);
            inptrs[i][j] += 4;
          }
        }

        // Compute the matrix W w
        for (int j = 0; j < 5; j++)
        {
          // Ww[0][j] = w[0][j]/4.0f;
          Ww[0][j] = vmulq_n_f32(w[0][j], 1.0f/4.0f);

          // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
          Ww[1][j] = vmulq_n_f32(
            vaddq_f32(
              vaddq_f32(
                vaddq_f32(w[1][j], w[0][j]),
                vaddq_f32(w[3][j], w[2][j])
              ),
              w[4][j]
            ),
            -1.0f/6.0f
          );

          // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
          // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f;
          Ww[2][j] = vmulq_n_f32(
            vsubq_f32(
              vaddq_f32(
                vsubq_f32(w[1][j], w[0][j]),
                vsubq_f32(w[3][j], w[2][j])
              ),
              w[4][j]
            ),
            1.0f/6.0f
          );

          // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
          Ww[3][j] = vmulq_n_f32(
            vmlaq_n_f32(
              vaddq_f32(
                vaddq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)),
                vaddq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
              ),
              w[4][j], 2.0f
            ),
            1.0f/3.0f
          );

          // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
          Ww[4][j] = vmulq_n_f32(
            vmlaq_n_f32(
              vaddq_f32(
                vsubq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)),
                vsubq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
              ),
              w[4][j], 2.0f
            ),
            1.0f/3.0f
          );

          // Ww[5][j] = w[4][j];
          Ww[5][j] = w[4][j];
        }

        // Compute V = W w WT
        for (int i = 0; i < 6; i++)
        {
          // V[i][0] = Ww[i][0]/4.0f;
          V[i][0] = vmulq_n_f32(Ww[i][0], 1.0f/4.0f);

          // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
          V[i][1] = vmulq_n_f32(
            vaddq_f32(
              vaddq_f32(
                vaddq_f32(Ww[i][1], Ww[i][0]),
                vaddq_f32(Ww[i][3], Ww[i][2])
              ),
              Ww[i][4]
            ),
            -1.0f/6.0f
          );

          // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
          // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f;
          V[i][2] = vmulq_n_f32(
            vsubq_f32(
              vaddq_f32(
                vsubq_f32(Ww[i][1], Ww[i][0]),
                vsubq_f32(Ww[i][3], Ww[i][2])
              ),
              Ww[i][4]
            ),
            1.0f/6.0f
          );

          // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][3] = vmulq_n_f32(
            vmlaq_n_f32(
              vaddq_f32(
                vaddq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)),
                vaddq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
              ),
              Ww[i][4], 2.0f
            ),
            1.0f/3.0f
          );

          // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][4] = vmulq_n_f32(
            vmlaq_n_f32(
              vaddq_f32(
                vsubq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)),
                vsubq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
              ),
              Ww[i][4], 2.0f
            ),
            1.0f/3.0f
          );

          // V[i][5] = Ww[i][4];
          V[i][5] = Ww[i][4];
        }

        // Store the transformed weights
        for (int i = 0, m = 0; i < 6; i++)
        {
          for (int j = 0; j < 6; j++, m++)
          {
            vst1q_f32(outptr + m*matrix_stride, V[i][j]);
          }
        }
        outptr += 4;
      }
#endif  // __aarch64__
#ifdef __arm_any__
      for (; channels_remaining >= 2; channels_remaining -= 2)
      {
        // Matrices used and computed in this kernel
        float32x2_t w[5][5], Ww[6][5], V[6][6];

        // Read weights
        for (int i = 0; i < 5; i++)
        {
          for (int j = 0; j < 5; j++)
          {
            w[i][j] = vld1_f32(inptrs[i][j]);
            inptrs[i][j] += 2;
          }
        }

        // Compute the matrix W w
        for (int j = 0; j < 5; j++)
        {
          // Ww[0][j] = w[0][j]/4.0f;
          Ww[0][j] = vmul_n_f32(w[0][j], 1.0f/4.0f);

          // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
          Ww[1][j] = vmul_n_f32(
            vadd_f32(
              vadd_f32(
                vadd_f32(w[1][j], w[0][j]),
                vadd_f32(w[3][j], w[2][j])
              ),
              w[4][j]
            ),
            -1.0f/6.0f
          );

          // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
          // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f;
          Ww[2][j] = vmul_n_f32(
            vsub_f32(
              vadd_f32(
                vsub_f32(w[1][j], w[0][j]),
                vsub_f32(w[3][j], w[2][j])
              ),
              w[4][j]
            ),
            1.0f/6.0f
          );

          // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
          Ww[3][j] = vmul_n_f32(
            vmla_n_f32(
              vadd_f32(
                vadd_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)),
                vadd_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
              ),
              w[4][j], 2.0f
            ),
            1.0f/3.0f
          );

          // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
          Ww[4][j] = vmul_n_f32(
            vmla_n_f32(
              vadd_f32(
                vsub_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)),
                vsub_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
              ),
              w[4][j], 2.0f
            ),
            1.0f/3.0f
          );

          // Ww[5][j] = w[4][j];
          Ww[5][j] = w[4][j];
        }

        // Compute V = W w WT
        for (int i = 0; i < 6; i++)
        {
          // V[i][0] = Ww[i][0]/4.0f;
          V[i][0] = vmul_n_f32(Ww[i][0], 1.0f/4.0f);

          // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
          V[i][1] = vmul_n_f32(
            vadd_f32(
              vadd_f32(
                vadd_f32(Ww[i][1], Ww[i][0]),
                vadd_f32(Ww[i][3], Ww[i][2])
              ),
              Ww[i][4]
            ),
            -1.0f/6.0f
          );

          // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
          // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f;
          V[i][2] = vmul_n_f32(
            vsub_f32(
              vadd_f32(
                vsub_f32(Ww[i][1], Ww[i][0]),
                vsub_f32(Ww[i][3], Ww[i][2])
              ),
              Ww[i][4]
            ),
            1.0f/6.0f
          );

          // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][3] = vmul_n_f32(
            vmla_n_f32(
              vadd_f32(
                vadd_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)),
                vadd_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
              ),
              Ww[i][4], 2.0f
            ),
            1.0f/3.0f
          );

          // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][4] = vmul_n_f32(
            vmla_n_f32(
              vadd_f32(
                vsub_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)),
                vsub_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
              ),
              Ww[i][4], 2.0f
            ),
            1.0f/3.0f
          );

          // V[i][5] = Ww[i][4];
          V[i][5] = Ww[i][4];
        }

        // Store the transformed weights
        for (int i = 0, m = 0; i < 6; i++)
        {
          for (int j = 0; j < 6; j++, m++)
          {
            vst1_f32(outptr + m*matrix_stride, V[i][j]);
          }
        }
        outptr += 2;
      }
#endif  // __arm_any__
      for (; channels_remaining; channels_remaining--)
      {
        // Matrices used and computed in this kernel
        float w[5][5], Ww[6][5], V[6][6];

        // Read weights
        for (int i = 0; i < 5; i++)
        {
          for (int j = 0; j < 5; j++)
          {
            w[i][j] = *(inptrs[i][j]++);
          }
        }

        // Compute the matrix W w
        for (int j = 0; j < 5; j++)
        {
          Ww[0][j] = w[0][j]/4.0f;
          Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
          Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
          Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
          Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
          Ww[5][j] = w[4][j];
        }

        // Compute V = W w WT
        for (int i = 0; i < 6; i++)
        {
          V[i][0] = Ww[i][0]/4.0f;
          V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
          V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
          V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
          V[i][5] = Ww[i][4];
        }

        // Store the transformed weights
        for (int i = 0, m = 0; i < 6; i++)
        {
          for (int j = 0; j < 6; j++, m++)
          {
            *(outptr + m*matrix_stride) = V[i][j];
          }
        }
        outptr++;
      }
    }
  }

  template <>
  template <>
  int WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
  {
    return 0;
  }

  template class WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>;
}  // namespace winograd
